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Deep Learning News
Medium
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The Problem with RNNs: Why We Moved to LSTMs
- Recurrent Neural Networks (RNNs) have loops in their architecture, allowing them to maintain a memory of past inputs.
- RNNs struggle with handling long-term dependencies due to the vanishing gradient problem.
- Another major challenge is unstable training caused by exploding gradients.
- Long Short-Term Memory (LSTM) networks have been developed to address these issues and can selectively remember or forget information over long sequences.
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Medium
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A Beginner’s Guide to Reinforcement Learning
- Reinforcement Learning (RL) is a branch of machine learning where an agent learns to make decisions by interacting with an environment.
- RL-powered agents learn to play video games, control self-driving cars, and even optimize financial portfolios.
- RL has deep roots in behavioral psychology, artificial intelligence, and control theory.
- RL is built upon a set of fundamental concepts that define how an agent interacts with its environment to learn optimal decision-making strategies.
- The fundamental assumption in RL is that any goal can be formulated as maximizing the cumulative reward over time.
- One of the biggest challenges in RL is deciding between exploitation and exploration.
- RL revolves around decision-making in an uncertain environment. To understand how an RL agent learns, we need to explore three fundamental concepts: state, action, and policy.
- A reward is the feedback signal that tells an agent how good or bad an action was in a given state.
- There are various types of RL agents including value-based agents, policy-based agents, actor-critic agents, model-free agents, and model-based agents.
- RL has numerous real-world applications in different domains including gaming, robotics, self-driving cars, healthcare, finance, energy management, e-commerce and conversational AI.
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VentureBeat
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OmniHuman: ByteDance’s new AI creates realistic videos from a single photo
- ByteDance researchers have developed OmniHuman, an AI system that can transform single photographs into realistic videos of people speaking and moving naturally.
- OmniHuman generates full-body videos that match speech with gestures and movements, surpassing previous AI models.
- The system was trained on over 18,700 hours of human video data using an innovative "omni-conditions" training approach.
- This advancement in AI-generated media has the potential to reshape digital entertainment, educational content creation, and communications.
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Medium
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### **How to Make Money with AI in 2025: Opportunities, Strategies, and Tools**
- AI offers numerous opportunities to make money, even without programming skills.
- Five profitable ways to make money with AI in 2025 are AI-generated content creation and sales, YouTube automation, AI-powered eCommerce, AI and affiliate marketing, and selling AI courses and consulting services.
- AI-generated content creation and sales can be monetized by personalizing and editing the AI-generated content to enhance quality and authenticity.
- YouTube automation allows running a channel without showing one's face, and success lies in choosing a high-demand niche and consistently publishing high-quality content.
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Medium
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Why eKYC Solutions are a Game Changer for Businesses Across All Sectors in 2025
- eKYC is a game-changer that boosts efficiency, cuts down fraud, and helps businesses stick to strict global rules like GDPR and AML standards.
- eKYC automates identity verification with tools like AI, biometrics, and OCR tech, reducing manual work, improving accuracy, and meeting compliance standards with ease.
- Various industries are embracing eKYC Solutions to improve operations, enhance security, and reduce risks.
- Countries and markets are adopting eKYC Solutions globally to enhance security, improve compliance, and create opportunities for businesses and individuals alike.
- eKYC ensures seamless, secure, and compliant identity checks as various industries become more digital.
- To stay ahead and ensure top-notch security and compliance, choosing a trusted eKYC provider like Faceplugin is key.
- At FacePlugin, their AI-powered solutions easily fit into businesses' current systems, offering scalability and security.
- FacePlugin offerings include on-premises or cloud-based deployment, flexible options tailored to specific business needs, and customizable solutions online or through mobile SDKs.
- Industries like banking, telecom, e-commerce, and healthcare are embracing eKYC Solutions to enhance customer experience, keep financial institutions competitive, fight fraud, ensure compliance, simplify processes, and secure sensitive information.
- eKYC is a must-have for businesses that want to stay competitive in today's fast-evolving digital landscape.
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Medium
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How I Made $500 a Week with AI Video Creation
- Revio is a user-friendly software that allows individuals to create engaging videos without requiring advanced technical skills.
- The software features hyper-realistic animations and lip-syncing capabilities, enabling users to transform static images or short clips into vibrant videos that resonate with viewers.
- Revio provides a full commercial license, allowing users to resell their creations and generate income by marketing videos on platforms like Fiverr and Upwork.
- The AI technology utilized by Revio enables users to create professional-grade videos quickly and export them in full HD quality, making it a cost-effective and efficient tool for content creation.
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Medium
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Model Interpretation for Image Generation Models: Understanding the Black Box
- Model interpretability enhances trust, especially in applications involving sensitive data.
- Interpretability in image generation is particularly challenging due to high dimensionality, complex latent spaces, and abstract features.
- Methods for interpreting image generation models include visualization of latent space, feature attribution, activation maximization, sensitivity analysis, and layer-wise relevance propagation (LRP).
- Interpretable GANs break down the generative process into more easily understandable components, such as explicitly mapping parts of the input to specific image attributes.
- Future research directions include interactive tools, explainability for creative use, and post-generation explanation.
- Model interpretability matters for trust and reliability, ethical concerns, debugging and improvement, and regulatory compliance.
- Understanding how an image generation model works is crucial in sectors like healthcare, finance, or autonomous vehicles.
- GANs and VAEs rely on "latent spaces" where each point corresponds to a possible image, making interpretation difficult.
- Interpretability techniques, like latent space visualization or feature attribution, help identify which parts of the model's input are most influential in generating certain image features.
- The development of more advanced and accessible techniques for interpreting image generation models is expected as AI continues to evolve.
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Medium
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The Surprising Role of AI-Driven Bots in Online Engagement
- AI-driven bots have transformed online engagement.
- They enhance user interaction but also risk genuine human connection.
- The quick response of bots is impressive, but they lack human warmth and empathy.
- The role of AI in online interactions is intriguing.
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Medium
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RoPE: Achieving Long-Context Understanding in LLMs
- The paper titled “RoFormer: Enhanced Transformer with Rotary Position Embedding” introduces a novel approach to positional encoding in transformer architectures through a method called Rotary Position Embedding (RoPE).
- The authors propose that the inner product of query qₘ and key kₙ be formulated by a function g, which takes only the word embeddings xₘ, xₙ, and their relative position m − n as input variables.
- They express this goal as: f(θᵤ − θₖ) ≈
- The complete math proof to arrive at this result could be done in another article.
- Rotations can be combined by adding their angles, following this rule: R(θᵢ)R(θⱼ) = R(θᵢ + θⱼ)
- This is where the relative position emerges! The matrix now represents a rotation by the difference in positions θⱼ- θᵢ, which directly encodes the relative position between tokens.
- RoFormer paper and the RoPE method proposed in it represent an advancement in transformer architecture by effectively leveraging positional information through rotary embeddings.
- This not only improves model performance but also addresses key limitations associated with traditional positional encodings, particularly in leveraging relative positions, handling long sequences and maintaining computational efficiency.
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Medium
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AI Agents Revolutionizing Industries: Ethical Debates, Applications & Future of Autonomous Tech
- AI agents, software programs designed to autonomously perform tasks, are revolutionizing industries.
- AI agents handle tasks independently, collect data, and execute actions to reach goals set for them.
- AI agents are bringing convenience and efficiency to small business owners.
- The future of AI agents holds promise as their capabilities continue to evolve.
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Medium
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Ethical AI & Humane Autonomous Technologies: Trends, Challenges & Solutions
- Humane autonomous technologies are reshaping our lives, blending innovation with ethical considerations.
- The integration of autonomous systems into our daily lives requires a balance between innovation and ethics.
- Understanding the importance of human interaction in the development of autonomous systems.
- Exploring the potential and challenges of humane autonomous technologies.
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Medium
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352
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Deepseek AI: A Cautionary Tale of Bias in AI Systems
- Bias in AI systems like Deepseek can generate misleading, skewed, or harmful content.
- Deepseek exhibits significant biases, making it unreliable for business applications.
- Implications of biased AI include poor business outcomes, misinformation, and regulatory challenges.
- AI integration should prioritize trustworthy and ethical solutions that align with business standards.
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Medium
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64
A Comprehensive Explanation of Fuzzy Systems and Their Types
- Fuzzy systems are computational models that use fuzzy logic to process uncertain information.
- Fuzzy systems consist of three main components: fuzzification, a knowledge base, and a decision unit.
- Types of fuzzy systems include fuzzy logic systems, Fuzzy Inference Systems (FIS), fuzzy clustering, fuzzy control systems, type-2 fuzzy logic systems, fuzzy neural networks, fuzzy decision support systems, fuzzy optimization systems, fuzzy time-series systems, and Fuzzy Cognitive Maps.
- Fuzzy logic systems apply fuzzy set theory and the principles of fuzzy logic to model and control uncertain information with applications like washing machines.
- Fuzzy Inference Systems (FIS) are used to model decision-making processes with the example application of a temperature control system that adjusts the thermostat based on fuzzy rules.
- Fuzzy clustering allows data points to belong to multiple clusters with varying degrees of membership with image segmentation being a useful application.
- Fuzzy control systems are designed to control dynamic systems using fuzzy logic to process inputs and provide continuous output decisions with the example of a fuzzy controller for an autonomous vehicle.
- Type-2 fuzzy logic systems extend traditional type-1 fuzzy logic systems by allowing the membership functions to be fuzzy themselves and are useful in robotics.
- Fuzzy Neural Networks combine fuzzy logic with neural networks with an example application being stock market prediction.
- Fuzzy Decision Support Systems use fuzzy logic to evaluate multiple alternatives based on various criteria like choosing investment options.
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Medium
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Top 10 Game-Changing Open Source Tools of 2024: Powering AI, Data & Development
- Airbyte is an open source data integration platform that connects applications, APIs, databases, and files.
- It is used for building ETL/ELT pipelines and is popular for handling unstructured and semi-structured data.
- With over 40,000 companies using its open-source core code, Airbyte offers accessibility and wide user adoption.
- The platform is available under the MIT and Elastic License 2.0, and its commercial license offers additional benefits and partner programs.
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Towards Data Science
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Show and Tell
- In this article, an implementation of the Deep Learning model proposed in the "Show and Tell: A Neural Image Caption Generator" paper using PyTorch has been discussed.
- The image captioning task can be done by combining CNN and RNN models.
- The paper proposed to use GoogLeNet and LSTM for the task.
- In PyTorch, the InceptionEncoder and LSTMDecoder classes are used for this purpose.
- The ShowAndTell class packages the encoder and decoder together, and can be used for training and inference.
- The EMBED_DER and LSTM_HIDDEN_DIM variables are set to 512.
- A pretrained GoogLeNet model is used for the encoder, and transferred learning method is used.
- The generate() method simultaneously processes image features and generates an appropriate token sequence.
- To do the post-processing, the sequence generated from the generate() method needs to be converted into a set of words.
- The process of the model is summarized with each set of necessary code explained in order
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